【速報】 IEEE EMBC2013

発表タイトル          ：Developing Stimulus Presentation on Mobile Devices for a Truly

Portable SSVEP-based BCI

著者                     ： Yu-Te Wang, Yijun Wang, Chung-Kuan Cheng, and Tzyy-Ping Jung

セッション名       ：Brain-computer Interface III

Abstract ：  This study integrates visual stimulus presentation　and near real-time data processing on a mobile device (e.g. a　Tablet or a cell-phone) to implement a steady-state visual　evoked potentials (SSVEP)-based brain-computer interface　(BCI). The goal of this study is to increase the practicability,　portability and ubiquity of an SSVEP-based BCI for daily use.　The accuracy of flickering frequencies on the mobile SSVEP　BCI system was tested against that on a laptop/desktop used in　our previous studies. This study then analyzed the power　spectrum density of the electroencephalogram signals elicited by　the visual stimuli rendered on the mobile BCIs. Finally, this　study performed an online test with the Tablet-based BCI　system and obtained an averaged information transfer rate of　33.87 bits/min in three subjects. The current integration leads to　a truly practical and ubiquitous SSVEP BCI on mobile devices　for real-life applications.

発表タイトル          ：　Independent Component Analysis of EEG-fMRI data for studying epilepsy and epileptic seizures

著者                  ： Tiziana Franchin, Maria G. Tana, Vittorio Cannatà, Sergio Cerutti, and Anna M. Bianchi
セッション名       ： Multimodal Blind Source Separation: Algorithms, Applications and Future Challenges
Abstruct            ： Here we present a method for classifying fMRI independent components (ICs) by using an optimized algorithm for the individuation of noisy signals from sources of interest. The method was applied to estimate brain activations fromcombined EEG‐fMRI data for the exploration of epilepsy.  Spatial ICA was performed using the above-mentioned optimized algorithm and other three popular algorithms. ICs were sorted considering the value: of the coefficients of determination R2, obtained from the multiple regression analysis with morphometric maps of cerebral matter; of thekurtosis, which features the signal energy. The validation of the method was performed comparing the brain activations obtained with those resulted using the General Linear Model (GLM). The ICA‐derived activations in different datasets comprised subareas of the GLM‐revealed activations, even if the volume and the shape of activated areas do not correspond exactly. The method proposed also detects additional negative regions implicated in a default mode of brain activity, and not clearly identified by GLM. Compared with a traditional GLM approach, the ICA one provides a flexible way to analyze fMRI data that reduces the assumptions placed upon the hemodynamic response of the brain and the temporal constrains.

発表タイトル          ：Evaluation of Classifier Topologies for the Real-time　Classification　of Simultaneous Limb Motions

著者                  ： Max Ortiz-Catalan, Rickard Brånemark, and Bo Håkansson
セッション名       ：EMG Models and Processing

Abstract ：  The prediction of motion intent through the　decoding of myoelectric signals has the potential to improve the　functionally of limb prostheses. Considerable research on　individual motion classifiers has been done to exploit this idea.　A drawback with the individual prediction approach, however,　is its limitation to serial control, which is slow, cumbersome,　and unnatural. In this work, different classifier topologies　suitable for the decoding of mixed classes, and thus capable of　predicting simultaneous motions, were investigated in real-time.　These topologies resulted in higher offline accuracies than　previously achieved, but more importantly, positive indications　of their suitability for real-time systems were found.　Furthermore, in order to facilitate further development,　benchmarking, and cooperation, the algorithms and data　generated in this study are freely available as part of　BioPatRec, an open source framework for the development of　advanced prosthetic control strategies.